Activity monitoring in home environments has become increasingly important and has the potential to support a broad array of applications including elder care, well-being management, and latchkey child safety. By tracking a sequence of meaningful activities and generating statistics for a person, it may be possible to monitor well-being and suggest behavioral changes that can improve health. In aging-in-place settings, such tracking can be helpful to understand whether established routines are still followed, since the absence of usual activities can be an important indicator for detecting falls and other situations of need.
Traditional activity identification approaches involve wearable sensors and specialized hardware installations. Sensors can be either attached to a person's body, or placed on target objects with which people interact. In one previous technique, an accelerometer is attached on human body to detect falls in Philips Lifeline. In another, a motion sensor is attached to a door to detect movement. Other previous techniques involve a wearable acoustic sensor for classifying activities, such as eating and coughing. Vision based systems can also be used to track user movements and gestures. These dedicated sensors can achieve fine-grained activity recognition. However, they need the installation and maintenance of dedicated sensors, which usually entail high costs and are thus not scalable.
A single wireless monitor used to detect human movement or location can also be used for activity recognition. The granularity of the activity can be inferred from these systems is either modest or fine-grained. However, these systems all require a specialized WiFi monitor for extracting the carrier wave.